H2O Cascade
The next generation of H2O Enterprise LLM Studio: a model foundry where agents distill, fine-tune and evaluate small models you own, where your data lives.
- H2O Cascade, the next generation of H2O Enterprise LLM Studio, is a model foundry where agents distill, fine-tune and evaluate small models you own, where your data lives.
- A frontier model or any OpenAI-compatible endpoint you approve generates, labels or cleans the training rows, and H2O Cascade fine-tunes a small model with LoRA, QLoRA, full training or reinforcement learning with an LLM judge.
- Every H2O Cascade experiment keeps the data, configuration, metrics, GPU cost and owner behind each model version.
- Teams test models in H2O Cascade's Model Zoo, then deploy production models to H2O MLOps or the LLM serving platform they already use.
- An AT&T ensemble of fine-tuned small language models reached 91% accuracy at 35% of the prior cost, and AT&T cut call-analytics processing time from 15 hours to 4.5.
How does H2O Cascade work as a model foundry?
Label in one tool, train in a notebook, track in a third, and every seam loses reproducibility. Cascade runs the whole loop in one place, with agents doing the busywork and your team approving each step.
- Generate the data and labelsA frontier model or any OpenAI-compatible endpoint you approve, including Enterprise h2oGPTe, generates, labels or cleans training rows, with per-job cost accounting.
- Fine-tune small modelsTune a small language model you own instead of paying per token: LoRA, QLoRA or full training on Danube, Llama, Qwen3, Gemma or Mistral.
- Learn without labelsReinforcement learning with an LLM judge: the judge scores each answer from 1 to 10, and the score is the reward, with GRPO underneath.
- Let agents run the loopSet a budget of experiments and AutoML searches the configurations. Ask KGM, the Kaggle Grandmaster agent, proposes the next experiment and explains why.
- Score it and trace itPerplexity, BLEU, LLM-as-a-Judge, QA accuracy, AUC and mAP, and every experiment keeps the data, configuration, metrics, GPU cost and owner behind it.
- Test, then deployModel Zoo gives your team a quick test endpoint on shared GPUs. For production, deploy to H2O MLOps or the LLM serving platform you already use.
From your data to a model you own, in four steps.
- 01Frontier model teachesA large model you point it at generates, labels or cleans the training rows: model distillation in practice, with the cost of each job recorded.
- 02Small model learnsLoRA, QLoRA or full fine-tuning on the backbone you choose, or reinforcement learning with an LLM judge when you have no labels.
- 03Evaluation decidesEvery run is scored, compared and traced to its data and configuration, and agents propose the next experiment from the results.
- 04Production servesTest the winner in Model Zoo, then deploy it to H2O MLOps or your own serving platform, inside your environment. The model is yours.
The producer side of the generative lane.
Cascade turns your data into models, and Enterprise h2oGPTe consumes them. Cascade tests models in its own Model Zoo and deploys production models to H2O MLOps or your own serving platform. Deliberate boundary: the Feature Store does not feed Cascade. Highlighted: Cascade and the open models it builds on.
Shipped, and safe to rely on
The Super Agent orders the model. Cascade builds it.
Cascade exposes an MCP server. That makes "fine-tune a classifier on these 80 topics and deploy it" a step the H2O AI Super Agent can take inside a larger plan, with the cost of the run attributed and the lineage recorded.
You operate it, or H2O AI operates it for you.
Same platform either way: the same capabilities, the same governance, and the same audit trail. The choice stays open, so you can switch between them as the business changes, without rebuilding what runs on top.
H2O AI Cloud for Government
The H2O AI Cloud for Government has achieved FedRAMP High Certification, delivering the highest standard of cloud security so federal agencies can harness predictive and generative AI without compromising data sovereignty or compliance.
Questions people ask before they call us.
Yes. H2O Cascade fine-tunes with reinforcement learning and an LLM judge: the judge scores each answer from 1 to 10, and the score is the reward, with GRPO underneath. H2O Cascade can also have a frontier model generate or label the training rows first. This is not RLHF in the strict sense.
Bring the workflow with the biggest per-token bill.
We will generate the data, tune a small model, and benchmark it against your current model on accuracy and cost.